In this paper, we investigate output accuracy for a Discrete Event Simulation (DES) model and Agent Based Simulation (ABS) model. The purpose of this investigation is to find out which of these simulation techniques is the best one for modelling human reactive behaviour in the retail sector. In order to study the output accuracy in both models, we have carried out a validation experiment in which we compared the results from our simulation models to the performance of a real system. Our experiment was carried out using a large UK department store as a case study.

A novel approach to represent learning in human decision behavior for evacuation scenarios is proposed under the context of an extended Belief-Desire-Intention framework. In particular, we focus on how a human adjusts his perception process (involving a Bayesian belief network) in Belief Module dynamically against his performance in predicting the environment as part of his decision planning function. To this end, a Q-learning algorithm (reinforcement learning algorithm) is employed and further developed.

The efficiency of current cargo screening processes at sea and air ports is unknown as no benchmarks exists against which they could be measured. Some manufacturer benchmarks exist for individual sensors but we have not found any benchmarks that take a holistic view of the screening procedures assessing a combination of sensors and also taking operator variability into account. Just adding up resources and manpower used is not an effective way for assessing systems where human decision-making and operator compliance to rules play a vital role.

Suppliers and retailers in the newsvendor setting need to submit their pricing and inventory decisions respectively, well before actual customer demand is realized. In the literature they have both been typically considered as perfectly rational optimizers, exclusively interested in their own respective benefits. Under the above set of conditions the wholesale price-only contract has long been analytically proven as inefficient.

Emergency Departments (EDs) require advanced support systems for monitoring and controlling their processes: clinical, operational, and financial. A prerequisite for such a system is comprehensive operational information (e.g. queueing times, busy resources,…), reliably portraying and predicting ED status as it evolves in time. To this end, simulation comes to the rescue, through a two-step procedure that is hereby proposed for supporting real-time ED control.

In many areas of science, like computer science or electrical engineering, modeling languages have been established, however, this is not the case in the field of discrete processes (Weilkiens 2006). There are two reasons which motivate such a development.

This panel seeks to initiate a discussion within the production system simulation community about a fundamental change in the way we think about, teach, and implement production system simulation. Today, production system simulation, while based on formal simulation languages, is largely an artistic process.

The awareness of the greenhousegas effect and rising energy prices lead to initiatives to improve energy efficiency. These initiatives range from micro-generation, energy storage and efficient appliances to controllers with optimization objectives. Although these technologies are promising, their introduction may rise further questions. The implementation of such initiatives may have a severe impact on the electricity infrastructure. If several of these initiatives are introduced in a combined way, it is difficult to analyse their overall impact.

This paper presents a methodology to study the effect of different resolution strategies on the value of the investment in a project-specific dispute resolution ladder (DRL) using option/real option theories from financial engineering, process centric modeling, and system dynamics methodology.

Unlike fossil-fueled generation, solar energy resources are geographically distributed and highly intermittent, which makes their direct control difficult and requires storage units. The goal of this research is to develop a flexible capacity planning tool, which will allow us to obtain a most economical mixture of capacities from solar generation as well as storage while meeting reliability requirements against fluctuating demand and weather conditions. The tool is based on hybrid (system dynamics and agent-based models) simulation and meta-heuristic optimization.